Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper presents an attempt to estimate cost effectiveness of the governments’ arrangements for prevention, limitation and overcoming the consequences of the spread of COVID-19 pandemic, as a result of which nearly quarter of a billion people were infected and almost 5 million died. Epidemic control measures, undertaken by almost all national governments, have ended in 6,7 percent of world GDP lost in 2020 and global fiscal deficit of 13,9% of the collective GDP for 2020. Hundreds of billions of SDR, dollars, yens and euro, additionally issued by IMF and national central banks simultaneously with rate cuts and preferential refinancing caused the growth of broad money supply in 2020 to 145,1 percent of global GDP. All of this could not but influence economics of all the countries. For the purpose of estimation of the consequences of such a policy, we endeavored to interpret governments’ activities as operations of a fictitious business entity (COVID Gov Inc) and explore business model of such a company.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it